Here are the Statistics Video Tutorials: 

 Bar Chart, Pie Chart, Frequency Tables:  In this lecture, we learn how to summarize a categorical variable. We discuss frequency tables, a proportion, a percentage, bar charts, and pie charts. These are likely not completely new ideas to you, but this video will help to put all these ideas together in the context of summarizing a single categorical variable. We have accompanying R videos on how to create bar chart and pie charts with R (Link Here)

 Histograms and Density Plots: In this statistics video, we learn about the idea of a histogram as well as a density plot. A histogram is a common plot to see, which helps describe the distribution of a single numeric variable. A (Kernel) Density plot may be less commonly known by name, but is a useful companion to a histogram; you may have seen the density plot without knowing their name. These plots are very useful in helping us to visualize and describe the distribution of a numeric (quantitative/continuous) variable. Here we will talk about what these plots display and why they are useful in statistics and in research. Also, watch this video on how to create and modify histograms with R (Link Here)  

Boxplots in Statistics: In this statistics lecture, we learn all about boxplots. We discuss what boxplots are and what do they show, how are outliers defined and what are the variations of boxplots in statistics. We have accompanying R videos on How to create boxplots in R (Link Here) and How to create Box Plots with Two Factors (Stratified Boxplots) in R (Link Here) 

 Describing Distributions: Center, Spread & Shape:  Here we introduce terminology and descriptive words for describing the shape, measure of center and measures of variability of a distribution of data using multiple examples. Before learning about different measures of center (e.g. mean, median) as well as measures of spread/variability (such as standard deviation, IQR, etc.) we want to learn about describing the shape, the center and spread of distribution using descriptive words. Describing a distribution using numbers is good in that it is an objective measure, but these can also miss important features. Describing a distribution using qualitative words is good in that it can capture nuances, although it is not objecting. Using the two together can help us capture the best of both worlds.

 Mean, Median and Mode in Statistics: In this statistics video lecture, we introduce the mean, median, mode, and other measures of "center" for a numeric variable. These different measures are simply different ways to determine the center, each with their own pros and cons. These likely aren't new ideas to you, although this video may help you think of them in new ways, as well as get used to some of the statistical notation that gets used.

Standard Deviation & Degrees of Freedom: In this statistics video we learn the underlying concept of the Sample Standard Deviation, and what it is actually measuring. We will also explain the concept of the Degrees of Freedom and why do you divide by (n-1)? Standard deviation gives us a measure of how far individual observations tend to move (or deviate) away from the mean, on average. In other words, on average how far does an observation tend to get from the mean? It is not quite the average deviation, but we can think of it that way conceptually.  While we present the standard deviation formula, the focus is on the concept of what the standard deviation is attempting to summarize. We believe this to be one of the best explanations of standard deviation that you will see. To calculate the standard deviation with R watch this video (Link Here); Reminder: Standard deviation gives us a measure of how far individual observations tend to move (or deviate) away from the mean, on average. In other words, on average how far does an observation tend to get from the mean? It is not quite the average deviation, but we can think of it that way conceptually.

Sample and Population in Statistics: In this tutorial, we learn the difference between sample and population in statistics and research with examples. Here we will learn about the difference and the connection between the distribution of a sample (sometimes referred to as the empirical distribution) and the theoretical probability distribution used to describe a population. We will also explain the concept of a sample mean vs a population mean, and so on. Sample and Population in statistics are important concepts to learn, as it helps to build a bridge between the theoretical world of probability distributions and population parameters and the real world of sample distributions, sample estimate or sample statistics.  Here are some of the accompanying videos: Probability Distributions with R (Link Here) and Making Plots and Calculate Summary Statistics with R (Link Here)